ReviewBriefings in bioinformatics2022
Multi-modality artificial intelligence in digital pathology.
Review in Briefings in bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 37 citations in OpenAlex.
- Artificial Intelligence in Lymphoma Histopathology: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Deep learning-based classification of gh-secreting pituitary neuroendocrine tumors granulation patterns using H&E-stained histopathology.BMC endocrine disorders · 2026Article
- A new era of precision diagnosis and treatment for lung cancer: artificial intelligence-driven multimodal data integration and clinical applications.Cell death & disease · 2026Review
- Artificial Intelligence in Prostate MRI: Addressing Current Limitations Through Emerging Technologies.Journal of magnetic resonance imaging : JMRI · 2026Review
- LIMPACAT: Multi-omics attention transformer for immune prediction in liver cancer using whole-slide imaging.PloS one · 2026Article
- AI-powered prediction model for neoadjuvant chemotherapy efficacy: comprehensive analysis of breast cancer histological images.NPJ precision oncology · 2025Article
- AI assistance enhances histopathologic distinction between sebaceous and squamous cell carcinoma of the eyelid.NPJ digital medicine · 2025Article
- Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis.Genomics, proteomics & bioinformatics · 2025Review
- Advancing precision medicine: the transformative role of artificial intelligence in immunogenomics, radiomics, and pathomics for biomarker discovery and immunotherapy optimization.Cancer biology & medicine · 2025Review
- Transforming cervical cancer pathological diagnosis through artificial intelligence: progress, performance, and barriers to clinical implementation.Frontiers in oncology · 2025Review
- Article
- Interactivity, humanness, and trust: a psychological approach to AI chatbot adoption in e-commerce.BMC psychology · 2024Article
- Predicting lymph node recurrence in cT1-2N0 tongue squamous cell carcinoma: collaboration between artificial intelligence and pathologists.The journal of pathology. Clinical research · 2024Article
- DLCNBC-SA: a model for assessing axillary lymph node metastasis status in early breast cancer patients.Quantitative imaging in medicine and surgery · 2024Article
- Perspective on quantitative phase imaging to improve precision cancer medicine.Journal of biomedical optics · 2024Article
- Physician Versus Large Language Model Chatbot Responses to Web-Based Questions From Autistic Patients in Chinese: Cross-Sectional Comparative Analysis.Journal of medical Internet research · 2024Observational
- Bringing CAM photosynthesis to the table: Paving the way for resilient and productive agricultural systems in a changing climate.Plant communications · 2024Review
- Digital pathology and multimodal learning on oncology data.BJR artificial intelligence · 2024Review
- Human-Machine Collaboration in Diagnostics: Exploring the Synergy in Clinical Imaging with Artificial Intelligence.Diagnostics (Basel, Switzerland) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 2 institutions in 2 countries.
Funding
Abstract
In common medical procedures, the time-consuming and expensive nature of obtaining test results plagues doctors and patients. Digital pathology research allows using computational technologies to manage data, presenting an opportunity to improve the efficiency of diagnosis and treatment. Artificial intelligence (AI) has a great advantage in the data analytics phase. Extensive research has shown that AI algorithms can produce more up-to-date and standardized conclusions for whole slide images. In conjunction with the development of high-throughput sequencing technologies, algorithms can integrate and analyze data from multiple modalities to explore the correspondence between morphological features and gene expression. This review investigates using the most popular image data, hematoxylin-eosin stained tissue slide images, to find a strategic solution for the imbalance of healthcare resources. The article focuses on the role that the development of deep learning technology has in assisting doctors' work and discusses the opportunities and challenges of AI.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.